AI-Trader

A professional, config-driven backtesting framework for algorithmic trading, built on Backtrader. Seamlessly test, optimize, and integrate trading strategies with Large Language Models (LLMs) across stocks, crypto, and forex markets.

Demo GIF

Key Features

Config-Driven Workflows: Define and manage backtests with version-controllable YAML files for reproducible results.
Seamless LLM Integration: Built-in MCP (Model Context Protocol) server allows AI assistants like Claude to run backtests, fetch data, and analyze strategies.
Multi-Market Support: Test strategies on US stocks, Taiwan stocks, cryptocurrencies, and forex.
Extensive Strategy Library: Comes with over 20 built-in strategies, from classic indicators to advanced adaptive models.
Powerful CLI: A rich command-line interface to run backtests, fetch market data, and list strategies.
Developer Friendly: Easily create and test custom strategies with simple helpers and a clear structure.

Quick Start

1. Installation

Option A: Install from PyPI (Recommended for using the CLI)

pip install ai-trader

Use this if you want to:

Use the CLI commands: ai-trader run, ai-trader fetch, ai-trader quick
Run backtests on your own data files
Use as a library in your Python projects

Option B: Install from Source (Recommended for examples and config templates)

git clone https://github.com/whchien/ai-trader.git
cd ai-trader
pip install -e .

Use this if you want to:

Run the config-based examples in config/backtest/
Use the example data files in data/
Run the example scripts in scripts/examples/
Contribute or customize strategies (Poetry users can run poetry install)

2. Run a Backtest via CLI

If you cloned from source, run a predefined backtest using a configuration file:

# Run a backtest from a config file (requires source installation)
ai-trader run config/backtest/classic/sma_example.yaml

Or, run a quick backtest on any data file (works with both pip and source installation):

# Quick backtest on your own data file
ai-trader quick CrossSMAStrategy your_data.csv --cash 100000

3. Fetch Market Data

Download historical data for any supported market:

# US Stock
ai-trader fetch TSM --market us_stock --start-date 2020-01-01

# Taiwan Stock (台灣股票)
ai-trader fetch 2330 --market tw_stock --start-date 2020-01-01

# Cryptocurrency
ai-trader fetch BTC-USD --market crypto --start-date 2020-01-01

Core Workflows

1. Configuration-Based Backtesting

The most robust way to run backtests is with a YAML config file.

my_backtest.yaml:

broker:
  cash: 1000000
  commission: 0.001425

data:
  file: "data/us_stock/TSM.csv"
  start_date: "2020-01-01"
  end_date: "2023-12-31"

strategy:
  class: "CrossSMAStrategy"
  params:
    fast: 10
    slow: 30

sizer:
  type: "percent"
  params:
    percents: 95

Run it:

ai-trader run my_backtest.yaml

See config/backtest/ for more examples.

2. Python-Based Backtesting

For more granular control or integration into other Python scripts.

Simple approach:

from ai_trader import run_backtest
from ai_trader.backtesting.strategies.classic.sma import CrossSMAStrategy

# Run backtest with example data
results = run_backtest(
    strategy=CrossSMAStrategy,
    data_source=None,  # Uses built-in example data
    cash=1000000,
    strategy_params={"fast": 10, "slow": 30}
)

Step-by-step control: See scripts/examples/02_step_by_step.py for a detailed example.

3. LLM Integration (MCP Server)

Run ai-trader as a server to let AI assistants interact with your backtesting engine.

Start the Server (for testing):

python -m ai_trader.mcp

Configure with Claude Desktop (Recommended):

Locate your Claude Desktop configuration file:
- macOS/Linux: ~/.config/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
Add the ai-trader MCP server to the mcpServers section:

{
  "mcpServers": {
    "ai-trader": {
      "command": "python3",
      "args": ["-m", "ai_trader.mcp"],
      "cwd": "/path/to/ai-trader"
    }
  }
}

Configuration Notes:

Replace /path/to/ai-trader with your actual ai-trader project directory
If using a virtual environment, use the full path to the Python executable: /path/to/.venv/bin/python3
Restart Claude Desktop after updating the config file

Once configured, you can use Claude to interact with your backtesting engine with natural language commands like:

"Run a backtest of the CrossSMAStrategy on TSM data from 2020-2022."
"List all available trading strategies."
"Fetch Apple stock data from 2021 to 2024."

Creating Custom Strategies

Create a new file in ai_trader/backtesting/strategies/classic/ and inherit from BaseStrategy.

# ai_trader/backtesting/strategies/classic/my_strategy.py
import backtrader as bt
from ai_trader.backtesting.strategies.base import BaseStrategy

class MyCustomStrategy(BaseStrategy):
    params = dict(period=20)

    def __init__(self):
        self.sma = bt.indicators.SMA(self.data.close, period=self.p.period)

    def next(self):
        if not self.position and self.data.close[0] > self.sma[0]:
            self.buy()
        elif self.position and self.data.close[0] < self.sma[0]:
            self.close()

The new strategy is automatically available to the CLI and run_backtest function.

Documentation & Resources

Strategy Examples: Details on built-in strategies.
Example Scripts: 5 complete working examples for different use cases.
Config Templates: YAML configuration templates.
Migration Guide: For upgrading from v0.1.x.

Contributing

Contributions are welcome! Feel free to report bugs, suggest features, or submit pull requests.

Show Your Support

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License

This project is licensed under the GNU General Public License v3 (GPL-3.0). See the LICENSE file for details.

ai-trader